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Mengchao Wang, Wang Qiang, Fan Jiang, Mu Xu

Recent advances in audio-driven portrait animation have demonstrated impressive capabilities. However, existing methods struggle to align with fine-grained human preferences across multiple dimensions, such as motion naturalness, lip-sync accuracy, and visual quality. This is due to the difficulty of optimizing among competing preference objectives, which often conflict with one another, and the scarcity of large-scale, high-quality datasets with multidimensional preference annotations. To address these, we first introduce Talking-Critic, a multimodal reward model that learns human-aligned reward functions to quantify how well generated videos satisfy multidimensional expectations. Leveraging this model, we curate Talking-NSQ, a large-scale multidimensional human preference dataset containing 410K preference pairs. Finally, we propose Timestep-Layer adaptive multi-expert Preference Optimization (TLPO), a novel framework for aligning diffusion-based portrait animation models with fine-grained, multidimensional preferences. TLPO decouples preferences into specialized expert modules, which are then fused across timesteps and network layers, enabling comprehensive, fine-grained enhancement across all dimensions without mutual interference. Experiments demonstrate that Talking-Critic significantly outperforms existing methods in aligning with human preference ratings. Meanwhile, TLPO achieves substantial improvements over baseline models in lip-sync accuracy, motion naturalness, and visual quality, exhibiting superior performance in both qualitative and quantitative evaluations.

Luyao Wang, Chuxin Wang, Qiao Li, Tianzhu Zhang

Recent advances in point cloud analysis have increasingly leveraged large-scale unlabeled data through self-supervised representation learning. Autoregressive models based on next-token prediction have shown strong performance, but they usually model point clouds as linear sequences, ignoring their inherent spatial structure. To address this limitation, we propose PointChain, a novel autoregressive paradigm inspired by human perception mechanisms, designed to better align with the structural properties of point cloud. Specifically, we introduce structural chain encoding, which models the understanding process as a global-to-local structural chain inference, preserving spatial relationships throughout the prediction sequence. During pre-training, we design two auxiliary tasks: a next-scale prediction task that encourages cross-scale reasoning, and a scale-level contrastive learning task that promotes semantic consistency across scales. These components guide the model to learn more discriminative and generalizable point cloud representations. Experiments on multiple benchmarks, using both Transformer and Mamba backbones, validate the effectiveness of our approach. PointChain achieves state-of-the-art performance on several downstream tasks, including 93.75% accuracy on the hardest split of ScanObjectNN without voting strategy.

Lin Wang, Shiliang Sun, Jing Zhao

To facilitate the large-scale deployment of autonomous driving in real-world scenarios, developing low-cost and high-performance 3D object detection systems has become a critical technical challenge. Although high-beam LiDARs provide denser point cloud data, their prohibitive hardware cost and high power consumption limit their practicality. In contrast, low-beam LiDARs offer advantages in terms of affordability and energy efficiency, but often suffer from inadequate perception accuracy due to their sparser point cloud data. This paper focuses on the task of multimodal 3D object detection with low-beam LiDARs, and proposes a novel approach that integrates temporal and spatial representation learning to enhance detection accuracy under sparser sensor conditions. Specifically, our approach comprises: (1) a Temporal Feature Prediction Learning (TFPL) module, which predicts the current BEV representation based on a sequence of historical BEV features; (2) a Spatial Feature Observation Learning (SFOL) module, which aligns BEV (Bird's-Eye-View) features from high-beam and low-beam LiDAR to enforce the low-beam features to approximate high-beam representations; (3) an Uncertainty-Aware Fusion (UAF) strategy, which performs feature-wise weighting between the predicted and observed BEV features by leveraging channel-wise variances, effectively mitigating perturbations in the learned BEV representations. Extensive experiments on the KITTI and nuScenes 3D object detection datasets demonstrate that the proposed approach significantly improves detection performance under low-beam LiDAR configurations.

Kunpeng Wang, Feifan Sun, Keke Chen

Multi-modal Salient Object Detection (SOD) shows an improvement over its uni-modal counterpart by exploiting the complementary benefits between modalities. However, this improvement relies on complete multi-modal information, which is difficult to be guaranteed in practice due to sensor failures and transmission errors. To address this issue, we propose a robust multi-modal SOD framework that enhances the adaptability to modality-missing conditions, while maintaining comparable performance in the modality-complete condition. Nevertheless, flexibly handling modality-missing and modality-complete cases and integrating their corresponding multi-modal features in a unified framework is non-trivial. To this end, we achieve this framework by designing a Cascaded Mixture-of-Experts (CMoE) network that sequentially incorporates missing-aware and multi-modal MoE. Specifically, the missing-aware MoE employs three modality-reconstruction experts with a soft router to adaptively reconstruct feature representations for both missing and available modalities, assisted by an expert modulation loss that guides the router to assign expert weights according to missing conditions. The multi-modal MoE adopts two homogeneous uni-modal experts with learned modality-specific knowledge tailored for integrating modality features, which are dynamically combined via the soft router. The cascaded architecture fully empowers CMoE with the flexibility across varying input cases. Extensive experiments on modality-missing and modality-complete conditions demonstrate the effectiveness of the proposed method.

Jingyang Wang, Hengyue Bi, Jingchao Cao, Feng Gao, Junyu Dong

The scarcity of paired data severely limits the performance and generalization of learning-based underwater image enhancement (UIE) methods. This challenge is particularly prominent in scenes with complex degradations. Semi-supervised learning has emerged as a promising solution by enabling the utilization of large-scale unlabeled data. However, its effectiveness is limited by the use of static, model-agnostic metrics for pseudo-label reliability assessment. To address this, we propose SEA-PACE, a novel semi-supervised framework that integrates model-aware uncertainty modeling and self-paced consistency learning to fully exploit unlabeled data for UIE. Specifically, we design a Model-Aware Reliability Estimator (MARE) that quantifies the uncertainty of the teacher model's predictions through Gaussian Process Regression in latent feature space. The resulting uncertainty is then transformed into reliability weights via a rank-based mapping. Additionally, we apply the Self-Paced Consistency Learning (SPCL) strategy that employs a loss-aware schedule to dynamically prioritize high-confidence pseudo-labels, gradually incorporating more challenging samples during training. Extensive experiments on several public UIE benchmarks demonstrate that SEA-PACE consistently surpasses state-of-the-art methods in both visual quality and generalization capability.

Jiayu Wang, Haoyu Bian, Haoran Sun, Shaoning Zeng

Image deraining is crucial for vision applications but is challenged by the complex multi-scale physics of rain and its coupling with scenes. To address this challenge, a novel approach inspired by multi-stage image restoration is proposed, incorporating Point Spread Function (PSF) mechanisms to reveal the image degradation process while combining dynamic physical modeling with sequential feature fusion transfer, named SD-PSFNet. Specifically, SD-PSFNet employs a sequential restoration architecture with three cascaded stages, allowing multiple dynamic evaluations and refinements of the degradation process estimation. The network utilizes components with learned PSF mechanisms to dynamically simulate rain streak optics, enabling effective rain-background separation while progressively enhancing outputs through novel PSF components at each stage. Additionally, SD-PSFNet incorporates adaptive gated fusion for optimal cross-stage feature integration, enabling sequential refinement from coarse rain removal to fine detail restoration. Our model achieves state-of-the-art PSNR/SSIM metrics on Rain100H (33.12dB/0.9371), RealRain-1k-L (42.28dB/0.9872), and RealRain-1k-H (41.08dB/0.9838). In summary, SD-PSFNet demonstrates excellent capability in complex scenes and dense rainfall conditions, providing a new physics-aware approach to image deraining.

Jiankang Wang, Zhihan Zhang, Zhihang Liu, Yang Li, Jiannan Ge, Hongtao Xie, Yongdong Zhang

Multimodal Large Language Models (MLLMs) have shown remarkable progress in temporal or spatial localization tasks, but struggle with joint spatio-temporal video grounding (STVG). We identify two key bottlenecks hindering this capability: (1) the sheer number of visual tokens makes long-range and fine-grained visual modeling challenging; (2) generating a long sequence of bounding boxes in text makes it hard to accurately align each box with its specific video frame. Distinct from prior efforts that rely on attaching complex modules, we argue for a more elegant paradigm that unlocks the inherent potential of MLLMs and leverages their strengths. To this end, we propose \textbf{\textit{SpaceVLLM}}, a MLLM equipped with spatio-temporal video grounding capabilities. Specifically, we propose Spatio-Temporal Aware Queries, interleaved with video frames, to guide the MLLM in capturing both static appearance and dynamic motion features. We further present a lightweight Query-Guided Space Head that maps queries to precise spatial coordinates, bypassing the need for direct textual coordinate generation and enabling the MLLM to focus on video understanding. To further facilitate research in this area, we propose an automated data synthesis pipeline to construct \textbf{V-STG} dataset, comprising 110K STVG instances. Extensive experiments show that \textit{SpaceVLLM} achieves the state-of-the-art performance on STVG benchmarks and maintains strong performance on various video understanding tasks, validating our approach's effectiveness.

Jian Wang, Lijun He, Yixing Yong, Haixia Bi, Fan Li

Modern autonomous driving (AD) systems leverage 3D object detection to perceive foreground objects in 3D environments for subsequent prediction and planning. Visual 3D detection based on RGB cameras provides a cost-effective solution compared to the LiDAR paradigm. While achieving promising detection accuracy, current deep neural network-based models remain highly susceptible to adversarial examples. The underlying safety concerns motivate us to investigate realistic adversarial attacks in AD scenarios. Previous work has demonstrated the feasibility of placing adversarial posters on the road surface to induce hallucinations in the detector. However, the unnatural appearance of the posters makes them easily noticeable by humans, and their fixed content can be readily targeted and defended. To address these limitations, we propose the AdvRoad to generate diverse road-style adversarial posters. The adversaries have naturalistic appearances resembling the road surface while compromising the detector to perceive non-existent objects at the attack locations. We employ a two-stage approach, termed Road-Style Adversary Generation and Scenario-Associated Adaptation, to maximize the attack effectiveness on the input scene while ensuring the natural appearance of the poster, allowing the attack to be carried out stealthily without drawing human attention. Extensive experiments show that AdvRoad generalizes well to different detectors, scenes, and spoofing locations. Moreover, physical attacks further demonstrate the practical threats in real-world environments.

Jiale Wang, Chen Zhao, Wei Ke, Tong Zhang

Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data generated by the same algorithms, leading to limited generalization to out-of-distribution data during inference. Therefore, in this paper, we introduce a novel diffusion-based paradigm that progressively injects noise into ground-truth data, simulating the noisy conditions for training learning-based RANSAC. To enhance data diversity, we incorporate Monte Carlo sampling into the diffusion paradigm, approximating diverse data distributions by introducing different types of randomness at multiple stages. We evaluate our approach in the context of feature matching through comprehensive experiments on the ScanNet and MegaDepth datasets. The experimental results demonstrate that our Monte Carlo diffusion mechanism significantly improves the generalization ability of learning-based RANSAC. We also develop extensive ablation studies that highlight the effectiveness of key components in our framework.

Jiahui Wang, Haiyue Zhu, Haoren Guo, Abdullah Al Mamun, Cheng Xiang, Tong Heng Lee

Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage. While effective, these methods face overreliance on pre-training, which hinders model flexibility and adaptability. Some models tried to avoid pre-training yet failed to capture ample information. In addition, current approaches focus on visual information in the support set and neglect or do not fully exploit other useful data, such as textual annotations. This inadequate utilization of support information impairs the performance of the model and restricts its zero-shot ability. To address these limitations, we present a novel pre-training-free network, named Efficient Point Cloud Semantic Segmentation for Few- and Zero-shot scenarios. Our EPSegFZ incorporates three key components. A Prototype-Enhanced Registers Attention (ProERA) module and a Dual Relative Positional Encoding (DRPE)-based cross-attention mechanism for improved feature extraction and accurate query-prototype correspondence construction without pre-training. A Language-Guided Prototype Embedding (LGPE) module that effectively leverages textual information from the support set to improve few-shot performance and enable zero-shot inference.Extensive experiments show that our method outperforms the state-of-the-art method by 5.68% and 3.82% on the S3DIS and ScanNet benchmarks, respectively.

Jiahao Wang, Zhongwei Jiang, Wenchao Sun, Jiaru Zhong, Haibao Yu, Yuner Zhang, Chenyang Lu, Chuang Zhang, Lei He, Shaobing Xu 等

Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, current approaches sharing dense Bird's-Eye-View (BEV) features are constrained by quadratically-scaling communication costs and the lack of flexibility and interpretability for precise alignment across asynchronous or disparate viewpoints. While emerging sparse query-based methods offer an alternative, they often suffer from inadequate geometric representations, suboptimal fusion strategies, and training instability. In this paper, we propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking that completely discards intermediate BEV representations. Our framework features a trio of innovations: a kinematic grounded instance query that uses an explicit state vector with 3D geometry and velocity for precise spatio-temporal alignment; a coarse-to-fine aggregation module that effectively integrates information from both matched and unmatched instances; and a cooperative instance denoising task that provides stable, abundant supervision to accelerate and stabilize training. Experiments on V2X-Seq and Griffin datasets show SparseCoop achieves state-of-the-art performance. Notably, it delivers this performance with superior computational efficiency and a highly competitive transmission cost, while showing remarkable robustness to real-world challenges like communication latency.

Jiahao Wang, Xiangyu Cao, Jiaru Zhong, Yuner Zhang, Zeyu Han, Haibao Yu, Chuang Zhang, Lei He, Shaobing Xu, Jianqiang Wang

While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is often impeded by significant economic barriers. Aerial-ground cooperation (AGC), which pairs ground vehicles with drones, presents a more economically viable and rapidly deployable alternative. However, this emerging field has been held back by a critical lack of high-quality public datasets and benchmarks. To bridge this gap, we present Griffin, a comprehensive AGC 3D perception dataset, featuring over 250 dynamic scenes (37k+ frames). It incorporates varied drone altitudes (20-60m), diverse weather conditions, realistic drone dynamics via CARLA-AirSim co-simulation, and critical occlusion-aware 3D annotations. Accompanying the dataset is a unified benchmarking framework for cooperative detection and tracking, with protocols to evaluate communication efficiency, altitude adaptability, and robustness to communication latency, data loss and localization noise. By experiments through different cooperative paradigms, we demonstrate the effectiveness and limitations of current methods and provide crucial insights for future research.

Jiahao Wang, Fang Liu, Licheng Jiao, Hao Wang, Shuo Li, Xinyi Wang, Lingling Li, Puhua Chen, Xu Liu

In recent years, the rapid progress of deep learning has driven notable advancements in satellite video tracking, a critical task for applications such as environmental monitoring, disaster management, and defense. Despite these strides, existing approaches remain constrained by their inability to handle dynamic challenges, such as target appearance variations, complex motion patterns, and occlusions. Traditional methods often suffer from static template matching or overly complex update mechanisms, compromising their robustness and practicality in real-world scenarios. To address these limitations, we propose a paradigm shift in satellite video tracking by integrating historical trajectory knowledge with visual features. This fusion enhances the tracker's perceptual understanding of targets over time, enabling more adaptive and resilient tracking. By aligning spatial, temporal, and cross-modal information, our approach effectively bridges the gap between fragmented observations and coherent tracking performance, even under challenging conditions like small target detection and cluttered backgrounds. Extensive experiments conducted on multiple satellite video tracking benchmarks demonstrate the superiority of our method, with HTTrack achieving success rates of 51.5% on SV248S, 52.9% on SatSOT, and 32.6% on VISO, significantly outperforming state-of-the-art trackers and marking a step forward in achieving robust, accurate, and scalable satellite video tracking.

Jiahao Wang, Fang Liu, Hao Wang, Shuo Li, Xinyi Wang, Puhua Chen

RGB-T tracking is increasingly deployed in safety-critical applications such as autonomous driving, surveillance, and rescue robotics, where tracking reliability is essential under adverse conditions. Although the fusion of RGB and thermal infrared (TIR) modalities offers improved robustness in low-light and occluded scenes, recent findings show that RGB-T trackers remain highly susceptible to subtle input perturbations, human-imperceptible modifications that exploit cross-modal inconsistencies to mislead tracking outputs. In real-world scenarios, such perturbations can arise from sensor spoofing, infrared camouflage, or physical-world attacks, posing serious risks to operational safety. To address this, we propose SFPT, a Semantic Feature Purification framework that enhances RGB-T tracking at the representation level. Rather than filtering corrupted inputs at the pixel level, SFPT introduces task-specific semantic anchors into the feature space to reinforce perturbation-invariant cues. These anchors are derived from descriptive language, interact with visual features to purify representations. To further suppress modality-specific interference, we design an Adaptive Perturbation-Guided Cross-Modal Fusion (APG-CMF) module, which leverages language and visual signals to estimate reliability and dynamically reweight cross-modal features, ensuring robust fusion under perturbation conditions. Extensive experiments under diverse perturbation conditions validate the effectiveness of our approach. Notably, SFPT maintains performance comparable to clean settings even when subjected to perturbations of strength 1/255 and 4/255, demonstrating strong resilience to real-world interference.

Huan Wang, Haoran Li, Yuxin Lin, Huaming Chen, Jun Yan, Lijuan Wang, Jiahua Shi, Qihao Xu, Yongting Hu, Yong Xu 等

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

Huabin Wang, Yu Yang, Xinran Zhong, Zilong Ling

Transmitting and receiving electromagnetic wave signals reflected back to the ground can detect the structure of subsurface defects. However, the imaging process of ground-penetrating radar (GPR) is highly susceptible to interference from complex underground environments, leading to nonlinear attenuation and noise. This makes it challenging to directly locate and identify defect types from raw reflected radar waveform images. Currently, mainstream methods of manual radar signal gain and filtering heavily rely on expert experience, while common end-to-end generative models are typically designed for optical images. This paper proposes a defect-guided Multi-window Gabor Transform Network (MGT-Net) for GPR B-Scan image reconstruction which achieves automatic gain and defect enhancement of raw GPR signals. Firstly, a Multi-window Gabor Transform Module (MGTM) is designed to effectively represent and extract spatial-frequency features of defects at different locations and of various types. Secondly, a defect guidance network (DG-Net) is constructed to accurately direct the reconstruction of defect areas and enhance the saliency and discriminability of defect features. Additionally, we construct a large-scale GPR B-Scan image dataset (GRD) containing 41,613 images across 7 defect categories. Experimental results show the superior performance of MGT-Net, achieving state-of-the-art (SOTA) SSIM of 81.72% ± 3.5% and PSNR of 30.50 ± 0.442.

Hua Wang, Xiaodan Zhang, Yanzhao Shi, Chengxin Zheng, Wanyu Zhang, Zhen Wang, Jianing Wang, Xiaobing Yu

Automated analysis of temporal changes in multimodal retinal images is critical for the prognostic assessment of ophthalmic diseases. Unlike traditional single-timepoint diagnosis, tracking longitudinal changes across multiple imaging modalities introduces significant data bias challenges: (1) Imbalanced modality samples compromise the integration of knowledge within minority modalities; (2) Heterogeneous visual patterns across modalities undermine the perception of disease-relevant biomarkers. To tackle these issues, we propose a Modality-Incremental Expert Aggregation Network (MoEA-Net), which unifies the inter-modal integration and intra-modal perception for enhanced retinal prognostic prediction. Specifically, we employ the large language model (LLM) with incremental LoRA layers for specific modalities to effectively integrate knowledge from imbalanced data. Besides, we introduce a Spatiotemporal-aware Expert (SAE) module to better perceive both the anatomical structures and longitudinal changes within modalities. By progressively combining the SAE module with incremental LoRA, MoEA-Net supports continual knowledge accumulation and improves accurate reasoning. Experimental results show that MoEA-Net achieves state-of-the-art performance on subretinal fluid change and visual recovery classification tasks, validating its effectiveness.

Hongyang Wang, Yichen Shi, Zhuofu Tao, Yuhao Gao, Liepiao Zhang, Xun Lin, Jun Feng, Xiaochen Yuan, Zitong Yu, Xiaochun Cao

Face anti-spoofing (FAS) is crucial for protecting facial recognition systems from presentation attacks. Previous methods approached this task as a classification problem, lacking interpretability and reasoning behind the predicted results. Recently, multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and decision-making in visual tasks. However, there is currently no universal and comprehensive MLLM and dataset specifically designed for FAS task. To address this gap, we propose FaceShield, a MLLM for FAS, along with the corresponding pre-training and supervised fine-tuning (SFT) datasets, FaceShield-pre10K and FaceShield-sft45K. FaceShield is capable of determining the authenticity of faces, identifying types of spoofing attacks, providing reasoning for its judgments, and detecting attack areas. Specifically, we employ spoof-aware vision perception (SAVP) that incorporates both the original image and auxiliary information based on prior knowledge. We then use an prompt-guided vision token masking (PVTM) strategy to random mask vision tokens, thereby improving the model's generalization ability. We conducted extensive experiments on three benchmark datasets, demonstrating that FaceShield significantly outperforms previous deep learning models and general MLLMs on four FAS tasks, i.e., coarse-grained classification, fine-grained classification, reasoning, and attack localization.

Hongjun Wang, Jiyuan Chen, Xuan Song, Yinqiang Zheng

Post-training quantization offers an efficient pathway to deploy super-resolution models, yet existing methods treat weight and activation quantization independently, missing their critical interplay. Through controlled experiments on SwinIR, we uncover a striking asymmetry: weight quantization primarily degrades structural similarity, while activation quantization disproportionately affects pixel-level accuracy. This stems from their distinct roles—weights encode learned restoration priors for textures and edges, whereas activations carry input-specific intensity information. Building on this insight, we propose HarmoQ, a unified framework that harmonizes quantization across components through three synergistic steps: structural residual calibration proactively adjusts weights to compensate for activation-induced detail loss, harmonized scale optimization analytically balances quantization difficulty via closed-form solutions, and adaptive boundary refinement iteratively maintains this balance during optimization. Experiments show HarmoQ achieves substantial gains under aggressive compression, outperforming prior art by 0.46 dB on Set5 at 2-bit while delivering 3.2× speedup and 4× memory reduction on A100 GPUs. This work provides the first systematic analysis of weight-activation coupling in super-resolution quantization and establishes a principled solution for efficient high-quality image restoration.

Hengkang Wang, Yang Liu, Huidong Liu, Chien-Chih Wang, Yanhui Guo, Hongdong Li, Bryan Wang, Ju Sun

Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they suffer from approximation errors during reverse diffusion and insufficient temporal consistency. Moreover, dealing with 3D video data, VR is inherently computationally intensive. In this paper, we advocate viewing the reverse process in DMs as a function and present a novel Maximum a Posterior (MAP) framework that directly parameterizes video frames in the seed space of DMs, eliminating approximation errors. We also introduce strategies to promote bilevel temporal consistency: semantic consistency by leveraging clustering structures in the seed space, and pixel-level consistency by progressive warping with optical flow refinements. Extensive experiments on multiple virtual reality tasks demonstrate superior visual quality and temporal consistency achieved by our method compared to the state-of-the-art.